Scheduling is an activity that is performed for all manners of enterprises and organizations. For many modern enterprises and organizations, scheduling is a very complex and compute-intensive task. This is because the scheduling process may need to take into account multiple disparate factors in order to fulfill the scheduling objectives.
For example, consider the typical request by a customer to a local utility or services company to schedule a repair visit. The utility/services company may have hundreds or thousands of repair personnel on its payroll, and there is the desire for the company to efficiently and effectively identify the specific repair person that is best suited to be scheduled for the job. This scheduling process may need to take into account numerous variables for the personnel to schedule, such as the availability, location, technical skill sets, and language ability.
Given this large set of different data, it is possible that the data that needs to be analyzed to perform the scheduling task is spread among multiple different database objects and storage entities. For example, it is possible that a calendaring database/table is used to store the calendars/schedules of the service personnel, an entirely separate database/table is used to track language abilities, and yet another database/table is used to track the technical training history of the personnel. Therefore, the process to search these databases/tables to perform scheduling may need to involve multiple costly operations (e.g., join operations) in order to determine the appropriate personnel to schedule for the task.
The problem is further complicated by the fact that this disparate set of data may potentially end up being an extremely large set of data, since many companies have hundreds or thousands of personnel for which data is stored in the databases/tables. This large set of data further increases the probable processing costs and time, as well as forcing large memory and compute requirements to perform the scheduling. The infrastructure requirements for enterprise schedules are extensive enough such that only connected-mode process can be used to perform the scheduling tasks, which is particularly problematic for enterprises that may need to perform scheduling in a disconnected or mobile mode.
In fact, given a large enough set of data that needs to be processed, it is quite possible that the scheduling activities can only be performed in a non-realtime manner, to allow batch processing of the data to perform the scheduling. In this situation, all the scheduling activity would need to happens well in advance and the field agents are notified of their jobs/tasks at the beginning of the day. However, in the today's dynamic world there is a need to be able to schedule activities on-demand. Most of the scheduler and/or optimizer in the market today do not have on-demand support.
Therefore, there is a need for an improved approach for implementing schedulers that address at least these problems.